Currently, more and more attention is paid to object detection, a representative of which is face detection. Face detection is the use of specific strategies on a randomly given image to search to see whether there is a human face contained therein: if yes, the position, size and countenance of the human face are returned. The human face is usually extremely rich in biometric information applicable in such fields as man-machine interaction, tracking and monitoring, and identification recognition etc., while the primary step in extracting information relevant to the human face is to position the region of the human face. This brings about an unusual significance to the technology of face detection and opens up a wide range for application. The practicality of face detection depends largely upon the enhancement of detection precision and detection speed.
There include in the hierarchical structure of a conventional detector, from an descending order, the detector, strong classifiers, weak classifiers, feature extraction and function mapping. In other words, the conventional detector comprises a plurality of strong classifiers, each of which comprises a plurality of weak classifiers, and each of which in turn comprises a feature extracting section and a mapping section. The feature extracting section extracts features, and the mapping section performs weak classification via methods like the one based on the look up table.
As presented in the following prior art documents, image brightness difference information (difference in values) is extracted during feature extraction in conventional face detection methods, the weak classifiers base thereon to judge whether the features constitute a face (also referred to as face exactness or weak classification), and the strong classifiers are prepared by combining these plural features.